Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine.
Purpose: This study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications of the associations between nutrient intake and anemia among university students. Methods: We employed K-means clustering analysis algorithm and Decision Tree (DT) technique to...
| Publicado en: | European Journal of Nutrition Vol. 63; no. 5; pp. 1635 - 1650 |
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| Autores principales: | , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179069388&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179069388 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14366207 CR0 jtl: European Journal of Nutrition issn: 14366207 maglogo: N pubinfo: dt: Aug2024 vid: 63 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179069388 176163906 179069388 179069388 10.1007/s00394-024-03360-8 179069388 ppf: 1635 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine. aug: au: Qasrawi, Radwan Badrasawi, Manal Al-Halawa, Diala Abu Polo, Stephanny Vicuna Khader, Rami Abu Al-Taweel, Haneen Alwafa, Reem Abu Zahdeh, Rana Hahn, Andreas Schuchardt, Jan Philipp affil: https://ror.org/04hym7e04 Department of Computer Science, Al-Quds University, Jerusalem, Palestine sug: subj: Students, College Palestine Machine Learning Methods Data Mining Methods Food Intake Nutrients Anemia, Iron Deficiency Blood Malnutrition Complications Anemia, Iron Deficiency Risk Factors Dietary Patterns Risk Assessment Women's Health Palestine Human Female Adolescence Adult Cross Sectional Studies Cluster Analysis Descriptive Statistics Decision Trees T-Tests Analysis of Variance Vitamins Minerals Micronutrients Malnutrition Proteins Anemia, Iron Deficiency Health Status Algorithms Funding Source Adolescent: 13-18 years Adult: 19-44 years Female ab: Purpose: This study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications of the associations between nutrient intake and anemia among university students. Methods: We employed K-means clustering analysis algorithm and Decision Tree (DT) technique to identify the association between anemia and vitamin and mineral intakes. We normalized and balanced the data based on anemia weighted clusters for improving ML models' accuracy. In addition, t-tests and Analysis of Variance (ANOVA) were performed to identify significant differences between the clusters. We evaluated the models on a balanced dataset of 755 female participants from the Hebron district in Palestine. Results: Our study found that 34.8% of the participants were anemic. The intake of various micronutrients (i.e., folate, Vit A, B5, B6, B12, C, E, Ca, Fe, and Mg) was below RDA/AI values, which indicated an overall unbalanced malnutrition in the present cohort. Anemia was significantly associated with intakes of energy, protein, fat, Vit B1, B5, B6, C, Mg, Cu and Zn. On the other hand, intakes of protein, Vit B2, B5, B6, C, E, choline, folate, phosphorus, Mn and Zn were significantly lower in anemic than in non-anemic subjects. DT classification models for vitamins and minerals (accuracy rate: 82.1%) identified an inverse association between intakes of Vit B2, B3, B5, B6, B12, E, folate, Zn, Mg, Fe and Mn and prevalence of anemia. Conclusions: Besides the nutrients commonly known to be linked to anemia—like folate, Vit B6, C, B12, or Fe—the cluster analyses in the present cohort of young female university students have also found choline, Vit E, B2, Zn, Mg, Mn, and phosphorus as additional nutrients that might relate to the development of anemia. Further research is needed to elucidate if the intake of these nutrients might influence the risk of anemia. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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